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More Deep Learning Models

Sebastian Thrun, Cezanne Camacho, Jay Alammar, Alexis Cook, Luis Serrano, Juan Delgado, and Ortal Arel

What's inside

Syllabus

Get a high-level overview of how fully-convolutional neural networks work, and see how they can be used to classify every pixel in an image.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Examines fully-convolutional neural networks, a key technology in image classification
Taught by recognized instructors in the field of deep learning
Requires no prerequisite knowledge, making it accessible to beginners
Provides a hands-on experience with interactive materials
Part of a larger series on deep learning, offering further exploration opportunities

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Activities

Coming soon We're preparing activities for More Deep Learning Models. These are activities you can do either before, during, or after a course.

Career center

Learners who complete More Deep Learning Models will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and implementing machine learning models. They work with data scientists to identify the right machine learning algorithms to use for a given problem, and then they build and tune the models to achieve the desired results. This course can help Machine Learning Engineers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Data Scientist
Data Scientists use their knowledge of statistics, mathematics, and computer science to extract insights from data. They work with businesses to identify the data that is most relevant to their needs, and then they develop machine learning models to analyze the data and make predictions. This course can help Data Scientists to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems. These systems use cameras and other sensors to capture images and videos, and then they use machine learning algorithms to analyze the images and videos to extract insights. This course can help Computer Vision Engineers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that is well-suited for computer vision tasks.
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with businesses to understand their needs, and then they design and develop software solutions that meet those needs. This course can help Software Engineers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, mathematics, and statistics. They develop new theories and algorithms, and they apply these theories and algorithms to solve real-world problems. This course can help Research Scientists to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Data Analyst
Data Analysts use their knowledge of statistics and computer science to analyze data and extract insights. They work with businesses to identify the data that is most relevant to their needs, and then they develop reports and visualizations that communicate the insights to decision-makers. This course can help Data Analysts to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Business Analyst
Business Analysts work with businesses to identify their needs and develop solutions to meet those needs. They use their knowledge of business processes and technology to develop solutions that are both effective and efficient. This course can help Business Analysts to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Product Manager
Product Managers are responsible for developing and managing products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course can help Product Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Quality Assurance Tester
Quality Assurance Testers are responsible for testing software to ensure that it is free of defects. They work with developers to identify and fix bugs. This course can help Quality Assurance Testers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Marketing Manager
Marketing Managers are responsible for developing and implementing marketing campaigns. They work with businesses to identify their target audience and develop marketing campaigns that reach that audience. This course can help Marketing Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Project Manager
Project Managers are responsible for planning and managing projects. They work with teams to ensure that projects are completed on time and within budget. This course can help Project Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Sales Manager
Sales Managers are responsible for developing and implementing sales strategies. They work with sales teams to identify and close deals. This course can help Sales Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Financial Analyst
Financial Analysts use their knowledge of finance and economics to analyze financial data and make investment recommendations. This course can help Financial Analysts to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Operations Manager
Operations Managers are responsible for planning and managing the day-to-day operations of a business. They work with employees to ensure that the business is running smoothly and efficiently. This course can help Operations Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Human Resources Manager
Human Resources Managers are responsible for managing the human resources of a business. They work with employees to recruit, hire, and train new employees. This course can help Human Resources Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in More Deep Learning Models.
Provides a comprehensive overview of deep learning, covering the latest advances in the field. It is an excellent resource for learners who want to gain a deeper understanding of the theoretical foundations of deep learning.
Provides a comprehensive overview of computer vision, including topics such as image processing, feature extraction, and object recognition. It valuable resource for learners who want to gain a broader understanding of the field.
Provides a comprehensive overview of pattern recognition and machine learning, including topics such as supervised and unsupervised learning, dimensionality reduction, and model selection. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of deep learning for natural language processing, covering topics such as word embeddings, sequence models, and attention mechanisms. It valuable resource for learners who want to gain a deeper understanding of this field.
Provides a practical guide to natural language processing with PyTorch, covering topics such as data preprocessing, feature engineering, model selection, and evaluation. It valuable resource for learners who want to gain hands-on experience with natural language processing.
Provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language understanding, and machine translation. It valuable resource for learners who want to gain a deeper understanding of this field.
Provides a practical guide to deep learning with Python, covering topics such as data preprocessing, feature engineering, model selection, and evaluation. It valuable resource for learners who want to gain hands-on experience with deep learning.
Provides a practical guide to TensorFlow for deep learning, covering topics such as data preprocessing, feature engineering, model selection, and evaluation. It valuable resource for learners who want to gain hands-on experience with TensorFlow.
Provides a practical guide to deep learning with R, covering topics such as data preprocessing, feature engineering, model selection, and evaluation. It valuable resource for learners who want to gain hands-on experience with deep learning in R.

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